• Journal of Industrial Technology
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• v.18
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• pp.379-384
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• 1998

Conventional short-term load forecasting techniques have limitation in their use on holidays due to dissimilar load behaviors of holidays and insufficiency of pattern data. Thus, a new short-term load forecasting method for special days in anomalous load conditions is proposed in this paper. The proposed method uses two Artificial Neural Networks(ANN); one is for the estimation of load curve, and the other is for the estimation of minimum and maximum value of load. The forecasting procedure is as follows. First, the normalized load curve is estimated by ANN. At next step, minimum and maximum values of load in a special day are estimated by another ANN. Finally, the estimate of load in a whole special day is obtained by combining these two outputs of ANNs. The proposed method shows a good performance, and it may be effectively applied to the practical situations.

• Korean Journal of Social Welfare
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• v.62 no.2
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• pp.279-306
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• 2010

This study is to compare and analyze the policy decision process between Korea Long-term Care Insurance and Japan's using policy network theory as an analysis tool, and to lead political and theoretical implications. The result of the study is summarized as follows. First, a policy agenda about Long-term Care problem for the Elderly set up by the government-leading both Korea and Japan. and the number of policy participation(actors) increased to characteristics of policy process stage. but there is a difference between Korea and Japan in a background of setting up policy agenda about Long-term Care problem. Second, interaction among policy actors is corporative from early policy agenda establishment stage in Korea and Japan. but it changes to dissenting or critical rapidly as being announced the frame of system to the people. Also, it shows that main policy actors play a role in policy making decision and the connection structures of network is similar to both countries although it has a difference in the interaction frequency. Fourth, although the number of policy actors and the extent of their opinion reflection to the policy effect are different, it shows that policy network pattern is very similar to from policy agenda setting stage to parliament(The National Assemble) policy decision stage as a result of government-leading policy process. The theoretical and political implications of this study are as follows. The number and the variety of policy participation, the importance of establishing opened-interaction system, lots of limitation of policy making-decision process of Japan Long-term Care system, and exclusively the government-leading network has demerits about the reflection of too much government's opinion to the policy outcomes.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.259-266
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• 2017

The massive traffic interferences in the wireless home IoT provides the reason for packet losses, and it degrades the QoS (Quality of Service) and throughput on the home network. This paper propose a new adaptive traffic interference control system, ATICS, for enhancing QoS and throughput for IoT services as detecting a traffic process and non-traffic process in the wireless home network. The proposed system control the traffic interferences as distinguishing the short-term traffic process and long-term traffic process by traffic characteristics in wireless home networks. The simulation results shows that the proposed scheme have more efficient traffic control performance than the other schemes.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.151-159
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• 2015

This article analyzes English abstracts of the articles published in Journal of the Korean Data & Information Science Society using text mining techniques. At first, term-document matrices are formed by various methods and then visualized by social network analysis. LDA (latent Dirichlet allocation) and CTM (correlated topic model) are also employed in order to extract topics from the abstracts. Performances of the topic models are compared via entropy for several numbers of topics and weighting methods to form term-document matrices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2612-2633
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• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• International Journal of Knowledge Content Development & Technology
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• v.9 no.3
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• pp.23-41
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• 2019

This study combines the concept of degree centrality in complex network with the Term Frequency $^*$ Proportional Document Frequency ($TF^*PDF$) algorithm; the combined method, called PMCN (PDF-Modified similarity and Complex Network), constructs relationship networks among sentences for writing news summaries. The PMCN method is a multi-document summarization extension of the ideas of Bun and Ishizuka (2002), who first published the $TF^*PDF$ algorithm for detecting hot topics. In their $TF^*PDF$ algorithm, Bun and Ishizuka defined the publisher of a news item as its channel. If the PDF weight of a term is higher than the weights of other terms, then the term is hotter than the other terms. However, this study attempts to develop summaries for news items. Because the $TF^*PDF$ algorithm summarizes daily news, PMCN replaces the concept of "channel" with "the date of the news event", and uses the resulting chronicle ordering for a multi-document summarization algorithm, of which the F-measure scores were 0.042 and 0.051 higher than LexRank for the famous d30001t and d30003t tasks, respectively.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.